The analysis of the case-only data
Given the same data, the following commands should yield almost identical results to the results being shown with any discrepancies being stochastic in nature. Some of these commands, however, can take a long time to run, so while we show the commands here as we originally ran them, results are often being read back from file. If you are trying to recapitulate these results using the exact data and code being used here and are running into problems or incongruitous results, please submit an issue, and we will address it as soon as possible.
These are the packages I will be using.
library(PreciseDist)
library(future)
library(doFuture)
library(readr)
library(heatmaply)This is the session info.
sessionInfo()
R version 3.4.4 (2018-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets
[7] methods base
other attached packages:
[1] heatmaply_0.15.2 viridis_0.5.1
[3] viridisLite_0.3.0 plotly_4.7.1
[5] ggplot2_3.0.0 readr_1.1.1
[7] doFuture_0.6.0 iterators_1.0.10
[9] foreach_1.4.4 future_1.9.0
[11] PreciseDist_0.0.0.9000
loaded via a namespace (and not attached):
[1] R.utils_2.6.0 tidyselect_0.2.4
[3] htmlwidgets_1.2 TSP_1.1-6
[5] trimcluster_0.1-2 grid_3.4.4
[7] ranger_0.10.1 Rtsne_0.13
[9] munsell_0.5.0 codetools_0.2-15
[11] SNFtool_2.3.0 miniUI_0.1.1.1
[13] misc3d_0.8-4 withr_2.1.2
[15] colorspace_1.3-2 longitudinalData_2.4.1
[17] Boruta_6.0.0 knitr_1.20
[19] geometry_0.3-6 stats4_3.4.4
[21] robustbase_0.93-1 dtw_1.20-1
[23] dimRed_0.1.0 DistatisR_1.0
[25] listenv_0.7.0 radix_0.5.0.9001
[27] DistributionUtils_0.5-1 rprojroot_1.3-2
[29] locpol_0.7-0 ipred_0.9-6
[31] randomForest_4.6-14 gclus_1.3.1
[33] diptest_0.75-7 R6_2.2.2
[35] seriation_1.2-3 fields_9.6
[37] rpivotTable_0.3.0 flexmix_2.3-14
[39] manipulateWidget_0.10.0 DRR_0.0.3
[41] bitops_1.0-6 assertthat_0.2.0
[43] promises_1.0.1 networkD3_0.4
[45] SDMTools_1.1-221 scales_1.0.0
[47] nnet_7.3-12 mmtsne_0.1.0
[49] gtable_0.2.0 ddalpha_1.3.4
[51] globals_0.12.1 spam_2.2-0
[53] timeDate_3043.102 rlang_0.2.2
[55] CVST_0.2-2 RcppRoll_0.3.0
[57] profileModel_0.5-9 splines_3.4.4
[59] lazyeval_0.2.1 ModelMetrics_1.1.0
[61] princurve_2.1.0 trelliscopejs_0.1.14
[63] broom_0.5.0 checkmate_1.8.5
[65] heatmap.plus_1.3 rgl_0.99.16
[67] yaml_2.2.0 reshape2_1.4.3
[69] abind_1.4-5 threejs_0.3.1
[71] crosstalk_1.0.0 backports_1.1.3
[73] httpuv_1.4.4.2 caret_6.0-80
[75] tools_3.4.4 lava_1.6.2
[77] infer_0.3.1 gplots_3.0.1
[79] RColorBrewer_1.1-2 proxy_0.4-22
[81] BiocGenerics_0.24.0 analogue_0.17-0
[83] Rcpp_0.12.18 splus2R_1.2-2
[85] plyr_1.8.4 visNetwork_2.0.4
[87] base64enc_0.1-3 progress_1.2.0
[89] purrr_0.2.5 prettyunits_1.0.2
[91] rpart_4.1-13 diffusr_0.1.4
[93] zoo_1.8-3 sfsmisc_1.1-2
[95] cluster_2.0.7-1 magrittr_1.5
[97] data.table_1.11.4 TSclust_1.2.4
[99] mvtnorm_1.0-8 whisker_0.3-2
[101] matrixStats_0.53.1 hms_0.4.2
[103] NetPreProc_1.1 mime_0.6
[105] evaluate_0.11 xtable_1.8-3
[107] mclust_5.4.1 gridExtra_2.3
[109] compiler_3.4.4 tibble_1.4.2
[111] maps_3.3.0 mgc_1.0.1
[113] KernSmooth_2.23-15 crayon_1.3.4
[115] R.oo_1.22.0 htmltools_0.3.6
[117] mgcv_1.8-23 later_0.7.3
[119] tidyr_0.8.1 RcppParallel_4.4.1
[121] lubridate_1.7.4 magic_1.5-8
[123] fpc_2.1-11 autocogs_0.0.1
[125] MASS_7.3-49 Matrix_1.2-14
[127] permute_0.9-4 gdata_2.18.0
[129] wmtsa_2.0-3 R.methodsS3_1.7.1
[131] dotCall64_1.0-0 bindr_0.1.1
[133] gower_0.1.2 igraph_1.2.1
[135] ifultools_2.0-4 pkgconfig_2.0.1
[137] registry_0.5 brglm_0.6.1
[139] ExPosition_2.8.19 philentropy_0.2.0
[141] microbenchmark_1.4-4 recipes_0.1.3
[143] clv_0.3-2.1 webshot_0.5.0
[145] prodlim_2018.04.18 LPStimeSeries_1.0-5
[147] stringr_1.3.1 digest_0.6.18
[149] pls_2.6-0 vegan_2.5-2
[151] graph_1.56.0 rmarkdown_1.10
[153] dendextend_1.8.0 uwot_0.0.0.9004
[155] kernlab_0.9-26 gtools_3.8.1
[157] modeltools_0.2-22 shiny_1.1.0
[159] nlme_3.1-131 glasso_1.10
[161] jsonlite_1.5 bindrcpp_0.2.2
[163] alluvial_0.1-2 TSdist_3.4
[165] pillar_1.3.0 lattice_0.20-35
[167] httr_1.3.1 DEoptimR_1.0-8
[169] survival_2.41-3 glue_1.3.0
[171] xts_0.11-0 prabclus_2.2-6
[173] class_7.3-14 stringi_1.2.3
[175] pdc_1.0.3 KODAMA_1.5
[177] rsample_0.0.2 caTools_1.17.1
[179] dplyr_0.7.6 hglasso_1.2
library(future)
library(doFuture)
options(future.globals.maxSize = +Inf)
registerDoFuture()
plan(multicore, workers = 16)
flow_case_dists <- flow_case_data %>%
as.matrix() %>%
precise_dist(
dists = "all_dists",
suffix = "",
file = "/home/brian/Desktop/flow/flow_case_dists.rds",
parallel = TRUE,
local_timeout = Inf,
verbose = TRUE
)
flow_case_dists <- read_rds("/home/brian/Desktop/flow/flow_case_dists.rds")
flow_case_distances <- flow_case_dists %>%
precise_transform(enforce_dist = TRUE)
library(future)
library(doFuture)
options(future.globals.maxSize = +Inf)
registerDoFuture()
plan(multicore, workers = 4)
flow_case_umap <- precise_umap(
data = flow_case_distances,
distance = TRUE,
n_neighbors = 5,
spread = 10,
min_dist = 0.1,
bandwidth = 1,
type = "plotly",
color_vec = NULL,
colors = NULL,
parallel = TRUE,
verbose = TRUE
)
precise_trellis(flow_case_umap, path = paste0(getwd(), "/trellis_flow_case_umap"), self_contained = TRUE)